Intelligent customer services based on a vector propagation on a click graph model

ABSTRACT

A query is received from a user at a data service engine. The query includes a string of characters. A number of candidate topics are identified by the data service engine based on the query. A similarity score is determined between the query and each of the plurality of candidate topics based on a Vector Propagation On a Click Graph (VPCG) model trained based on user click data. A number of candidate topics are ranked based on the similarity scores. One or more topics are selected from the ranked candidate topics. The selected topics are outputted via a user interface (UI).

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/390,173, filed on Apr. 22, 2019, which is a continuation of PCTApplication No. PCT/CN2018/111714, filed on Oct. 24, 2018, and eachapplication is hereby incorporated by reference in its entirety.

TECHNICAL FIELD

This specification generally relates to natural language processing,especially in intelligent customer services.

BACKGROUND

As artificial intelligence (AI) has become increasingly popular, peopleare expecting more and more related products and services, such asrobotic writing, robotic composition, and self-driving cars. One of therepresentative applications in the AI field is the intelligent customerservice, which has been integrated into various aspects of people'slives. Intelligent customer service can communicate with users andautomatically reply to user questions or queries about products orservices to reduce the cost of customer service operations.

Natural language processing technology (NLP) helps enterprises to buildintelligent customer service robots. NLP can improve service efficiency,and reduce the cost of manual services, helping companies to achieve asuccessful transition from traditional call centers to intelligentcustomer contact centers.

SUMMARY

Implementations of the present disclosure are generally directed tonatural language processing in intelligent customer services. Moreparticularly, implementations of the present disclosure are directed tointelligent customer services based on Vector Propagation on a ClickGraph (VPCG) models.

In a general implementation, a query is received from a user at a dataservice engine. The query includes a string of characters. A number ofcandidate topics are identified by the data service engine based on thequery. A similarity score is determined between the query and each ofthe plurality of candidate topics based on a Vector Propagation On aClick Graph (VPCG) model trained based on user click data. A number ofcandidate topics are ranked based on the similarity scores. One or moretopics are selected from the ranked candidate topics. The selectedtopics are outputted via a user interface (UI).

Particular implementations of the subject matter described in thisdisclosure can be implemented so as to realize one or more of thefollowing advantages. For example, the described subject matter canprovide more relevant and accurate predictions of customer needs orintents, and solve customer questions, helping improve user experienceand user satisfaction.

It is appreciated that methods in accordance with the present disclosurecan include any combination of the aspects and features describedherein. That is, methods in accordance with the present disclosure arenot limited to the combinations of aspects and features specificallydescribed herein, but also may include any combination of the aspectsand features provided.

The details of one or more implementations of the present disclosure areset forth in the accompanying drawings and the description below. Otherfeatures and advantages of the present disclosure will be apparent fromthe description and drawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram illustrating an example environment forproviding intelligent customer services based on Vector Propagation On aClick Graph (VPCG) models, according to an implementation of the presentdisclosure.

FIG. 2 is a block diagram illustrating an example process for training aVPCG model based on user click data, according to an implementation ofthe present disclosure.

FIG. 3 is a flow chart illustrating an example process for calculating ascore of a user query q and a candidate knowledge point topic d using atrained VPCG model, according to an implementation of the presentdisclosure.

FIG. 4 is a block diagram illustrating an example system providingintelligent customer services based on a VPCG model, according to animplementation of the present disclosure.

FIG. 5 is a flow chart illustrating an example process for providingintelligent customer services based on a VPCG model, according to animplementation of the present disclosure.

FIG. 6 is a plot illustrating a performance comparison betweenintelligent customer services based on a VPCG model and aDeep-Semantic-Similarity-Model (DSSM), according to an implementation ofthe present disclosure.

FIG. 7 is a block diagram illustrating an example computer system usedto provide computational functionalities associated with describedalgorithms, methods, functions, processes, flows, and procedures asdescribed in the instant disclosure, according to an implementation ofthe present disclosure.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

The following detailed description describes intelligent customerservices based on Vector Propagation On a Click Graph (VPCG) models, andis presented to enable any person skilled in the art to make and use thedisclosed subject matter in the context of one or more particularimplementations. Various modifications, alterations, and permutations ofthe disclosed implementations can be made and will be readily apparentto those of ordinary skill in the art, and the general principlesdefined may be applied to other implementations and applications,without departing from scope of the disclosure. In some instances,details unnecessary to obtain an understanding of the described subjectmatter may be omitted so as to not obscure one or more describedimplementations with unnecessary detail and inasmuch as such details arewithin the skill of one of ordinary skill in the art. The presentdisclosure is not intended to be limited to the described or illustratedimplementations, but to be accorded the widest scope consistent with thedescribed principles and features.

Intelligent customer service systems can interact with customers (orusers) and provide automatic customer services by computer-implementedtechniques. Example intelligent customer service systems can beimplemented as, for example, a customer service robot or a question andanswer (Q&A) system.

Existing technologies typically first analyze an input question and tryto understand what the question is about, and then make a preliminaryanalysis on which method to use to answer the question. Some preliminarysearches can be subsequently made and alternative answers can begenerated. All generated answers will be scored. The answer having thehighest score is considered to be the most useful and will be returnedto the customer. Some Q&A systems determine categories of the questionsor relevant knowledge fields based on the user questions usingalgorithms that only rely on the language expressed by the user. Thesesystems suffer poor categorization results due to their deficiency inunderstanding or inferring user intents. For example, when usingintelligent customer services, users are more likely to use colloquialand simplified languages during their interactions with the customerservice robots. More than 50% of the questions asked by customerscontain 10 or fewer words, especially in the cases where the customersinitiate the questions. Relying solely on the user's literal expressionand traditional natural language processing (NLP) techniques to solvecustomers' problems has difficulties and limitations. With the increaseof the number of customers using e-commerce applications, the number ofquestions asked by the customers and the complexity of such questionsincrease accordingly, posing additional challenges in providingintelligent customer services catered to user needs.

Implementations of this disclosure introduce intelligent customerservices based on Vector Propagation On a Click Graph (VPCG) models toprovide a more accurate prediction or determination of user need orintent in order to respond to user queries. The describedimplementations apply a VPCG model that is trained based on click datato, for example, a scoring process that calculates scores of thecustomer's questions and corresponding answers. The disclosedimplementations can be integrated into a Q&A engine to achieve improvedcustomer service.

In some implementations, in the scenario of customer service Q&Ainteractions, when uncertain about the answer to a customer's question,the intelligent customer service system can provide the customer with anumber of (e.g., 3) answers and allow the customer to choose the mostrelevant answer to the customer's question. The system can thus collecta large number of different questions raised by different users, andtheir respective clicks or selections of the answers. The collection ofthe questions and selected answers constitutes user click data. Thedisclosed techniques leverage such data for mining user intents andmatching correct topics or subject matters desired by the users.

In some implementations, the disclosed system uses a bipartite graph tomodel the user click data. The resulting bipartite graph can be referredto as a click graph. Vector propagation can be applied to the clickgraph to obtain vector representations of user queries and candidatetopics. The topics can include, for example, the topic dealt with or thesubject represented in the user's query. For example, the topics caninclude titles, categories, classifications, or other subject mattersbased on content or knowledge points in the user's query. As an example,a user query q can be “how to withdraw funds?”, where the topics caninclude “cash out,” “redemption,” “bond,” and other subject matter.

Based on the user click data, a VPCG model can be built that includes avector representation of each of the user's queries and topics usingword elements in a vocabulary. In some implementations, to extend theapplication of the VPCG model, a set of ngrams (also referred to as anngram dictionary) can also be trained based on the user click data toobtain a vector representation of each ngram using the elements in thevocabulary. In the field of computational linguistics, an ngram can be acontinuous sequence of n items from a given sample of text or speech.The items can be phonemes, syllables, letters, words, or base pairsaccording to the application.

Upon receiving a new query from a user, the disclosed system can predicta vector representation of the new query using the VPCG model, forexample, based on the obtained vector presentations of the user queriesor the vector representations of the ngrams. A score can be calculatedbased on the vector representation of the new query relative to thevector representation of each candidate topic. The calculated scores canbe integrated into a search and ranking engine to help provide a bettermatch between returned topics corresponding to the user's query. As aresult, the intelligent customer system can provide enhanced userexperience by showcasing a better understanding of the user's needs andintents.

FIG. 1 is a block diagram illustrating an example environment 100 forproviding intelligent customer services based on Vector Propagation On aClick Graph (VPCG) models, according to an implementation of the presentdisclosure. The intelligent customer services can be provided, forexample, by a data service engine that provides customers with answersto their questions. The example environment 100 includes a user 102, acomputer 104, a network 106, and a back-end system 108. The exampleenvironment 100 can include additional users, computers, networks,systems, or other components. The example environment 100 can beconfigured in another manner in some implementations.

In some implementations, the network 106 includes a local area network(LAN), wide area network (WAN), the Internet, or a combination of theseor other networks. The network 106 can include one or more of a wirelessnetwork or wireline networks. The network 106 connects computing devices(e.g., the computer 104) and back-end systems (e.g., the back-end system108). In some implementations, the network 106 can be accessed over awired and/or a wireless communications link.

In the depicted example, the back-end system 108 includes at least oneserver system 110 and a data store 112. In some implementations, theback-end system 108 provides access to one or more computer-implementeddata services with which the computer 104 may interact. Thecomputer-implemented data services may be hosted on, for example, the atleast one server system 110 and the data store 112. Thecomputer-implemented data services may include, for example, a Q&A dataservice that may be used by the computer 104 to provide a user withanswers based on the user's questions and collected click data. Forexample, as part of the e-commerce financial service, the server system110 may generate one or more answers corresponding to the user 102'squestions regarding getting a small loan service.

In some implementations, the computer 104 sends the user 102's questionto the server system 110 for obtaining corresponding answers. The serversystem 110 analyzes the received question and matches the content of thequestion to one or more topics stored in the data store 112. The serversystem 110 can provide intelligent customer services based on VectorPropagation On a Click Graph (VPCG) models in the example environment100 dynamically, for example, by searching for the matched topics inreal-time.

In some implementations, the back-end system 108 includes computersystems employing clustered computers and components to act as a singlepool of seamless resources when accessed through the network 106. Forexample, such implementations may be used in a data center, cloudcomputing, storage area network (SAN), and network attached storage(NAS) applications. In some implementations, the back-end system 108 isdeployed and provides computer-implemented services through a virtualmachine(s).

FIG. 2 is a block diagram illustrating an example VPCG model 200 trainedbased on user click data, according to an implementation of the presentdisclosure. As illustrated in FIG. 2, the VPCG model 200 includes aclick graph with a set of nodes 202 a and 202 b (collectively, querynodes 202) that represent user queries q₁, and q₂, respectively, andanother set of nodes 204 a, 204 b and 204 c (collectively, topic nodes204) representing topics d₁, d₂, and d₃, respectively. The topic d canbe, for example, one or more words, phrases, or other collections ofword elements that represent a knowledge-point or a context-basedsubject matter. For example, the topics d₁, d₂, and d₃ can be “cashout,” “redemption,” and “bond,” respectively.

Each edge between the query nodes 202 and the topic nodes 204, such asedge 206 a, 206 b, 206 c, 206 d, or 206 e have a weight or valuerepresenting the number of user clicks (or selections) of a particulartopic (or subject matter) corresponding to a particular query of theuser. Because the click behavior of the same question for differentusers may be different, the weight values for different edges can bedifferent.

In the exemplary embodiment illustrated by FIG. 2, there are three edges206 a, 206 b, and 206 c connecting user query q₁ 202 a and topics d₁ 204a, d₂ 204 b and d₃ 204 c. The edge 206 a has a weight value 4, the edge206 b has a weight value 2, and the edge 206 c has a weight value 1.This indicates that when asking the same query q₁ and provided withcandidate topics d₁, d₂, and d₃, four users click the topic d₁ 204 a,two users click the topic d₂ 204 b, and one user clicks the topic d₃ 204c. That is, for the same question query q₁, four users consider topic d₁204 a is most relevant to their question, two users think topics d₂ 204b is most relevant to their question, and one user considers topic d₃204 b to be most relevant to their question.

In the above example where q₁ represents the query from a user “how towithdraw funds?”, and the candidate topics d₁, d₂, and d₃ topic can be“cash out,” “redemption,” and “bond,” respectively, the weights foredges 206 a, 206 b, and 206 c are 4, 2, and 1, respectively. Thisindicates after a group of customers asked the same question “how towithdraw funds?”, the system provided them with three candidateknowledge point topics, “cash out,” “redemption,” and “bond” and let thecustomers to choose the one most relevant to their questions. Among thegroup of customers, four of them chose the knowledge point topic “cashout,” two customers chose “redemption,” and one customer chose “bond.”

In some implementations, each of the user query q and topic d can berepresented as a vector of word elements in a vocabulary. The vocabularycan include a dictionary of letters, words, phrases, or other entriesthat form the basis for vector representation of a longer string ofcharacters (e.g., a query, a topic, or an ngram). In someimplementations, the vector representation of each user query q can beinitialized based on word elements in the vocabulary in a one-hot formator in another manner. Take the user query q_(i) as an example. Afterquery q_(i) has been broken into segments and removed irrelevant andmeaningless characters or words in each segment, query q_(i) can berepresented as a vector q₁: w₁, w₂, w₄, w₆, w_(k)ϵW, k=1, 2, . . . , v,where W is the vocabulary, a set of word elements, and the size ordimension of the set W is v. In some implementations, query q_(i) canhave a sparse representation q_(i): (w₁:1, w₂:1, w₄:1, w₆:1).

In some implementations, after a vector representation of each userquery q₁, q₂, . . . , q_(n) in the user click data is generated, avector representation of each topic d in the user click data can also begenerated based on the click bipartite graph in a similar manner. Forexample, a topic d_(i) can be represented as a vector of the wordelements based on the word elements in the same vocabulary. As anexample, the topic d_(i) may have a sparse representation d_(i):(w_(i):1, w_(j):1), where w_(i), w_(j)ϵW.

In some implementations, the vector representations of each user query qand topic d can be obtained in an iterative manner. For example, thegenerated vector representations of the topic d are reversely propagatedto update the vector representations of the user query q, and as such,one iteration is completed. Equations (1) and (2) below show examples ofthe vector representations of the user query q and topic d in the nthiteration:

$\begin{matrix}{d_{j}^{(n)} = {\frac{1}{{{\sum_{i = 1}^{Q}{C_{i,j} \cdot q_{i}^{({n - 1})}}}}_{2}}{\sum_{i = 1}^{Q}{C_{i,j} \cdot q_{i}^{({n - 1})}}}}} & {{eq}.\mspace{14mu} 1} \\{q_{i}^{(n)} = {\frac{1}{{{\sum_{j = 1}^{D}{C_{i,j} \cdot q_{j}^{({n - 1})}}}}_{2}}{\sum_{j = 1}^{D}{C_{i,j} \cdot d_{j}^{(n)}}}}} & {{eq}.\mspace{14mu} 2}\end{matrix}$

where outputs d_(j) ^((n)) and q_(i) ^((n)) are the vectorrepresentation of topic d_(j) and user query q_(i) after the nthiteration. If it is the first iteration, q_(i) ⁰ represents the initialvalue of the vector representation of q_(i). C_(i,j) indicates theweight of the edge between q_(i) and d_(j) derived based on user clickdata as described with respect to the bipartite graph 200 in FIG. 2. Forexample, C_(i,j) indicates the total number of user clicks or selectionsof topic d_(i) for the user query q_(i) in the user click data. If thereis no edge connecting q_(i) and d_(j), the value is 0. Q represents thetotal number of user queries in the user click data, i.e., q_(i), i=1,2, . . . , Q. D represents the total number of topics (e.g., knowledgepoint title vectors) in the user click data, i.e., d_(j), j=1, 2, . . ., D.

The total number of iterations can be, for example, pre-determined. Forexample, the iteration algorithm can be terminated when n=5, that is,iterate 5 times. After the iterative process ends, the vectorrepresentations of each user query q and topic d in the user click datacan be generated.

Typically, queries from different users vary. The user queries in theuser click data may not cover all possible questions users might ask. Togeneralize, in some implementations, a set of ngrams can be trainedbased on the user query in the user click data so that a new user querycan be represented based on the set of ngrams. For example, the new userquery q can be represented by a sequence, a weighted sum, or anothercombination of one or more ngrams. As illustrated in FIG. 2, nodes 214a, 214 b, 214 c, and 214 d represent ngram1, ngram2, ngram3, and ngram4,respectively. The ngram can be, for example, a completed word or phrasethat appears frequently in customer questions. As an example, the query“how to withdraw funds” can be segmented into two ngrams, “how to” and“withdraw fund.”

To convert a user query q into a representation of ngrams, a set G ofngrams (i.e., an ngram dictionary) can be obtained. The set G can be thefull set of candidate ngrams used for representations of the user queryq. In some implementations, each of the ngrams has an order up to 3. Thengrams can be sorted by the frequency of their occurrences. In someimplementations, the set G can include a number of ngrams having thehighest frequencies of occurrence. For example, the set G can includethe g most frequently occurring ngrams. In this case, the size of set Gis g, i.e., |G|=g.

In some implementations, the user query q can be represented using thengrams in set G according to, for example, a forward maximum matchalgorithm or another algorithm. In some implementations, the user queryq can be represented using the ngrams in the ngram set G. FIG. 2 showsan ngram set G including ngram₁ 214 a, ngram₂ 214 b, ngram₃ 214 c, andngram₄ 214 d. The ngram set G can include additional or differentngrams. As illustrated, query q₁ can be represented using ngram₁,ngram₂, and ngram₃, and query q₂ can be represented using ngram₃ andngram₄.

Using the previously described iteration method, the vectorrepresentation of query q can be propagated reversely to ngram,resulting in a vector representation of the ngram in terms of the userqueries q_(i), i=1, 2, . . . , N. Since the vector representation ofeach user query q is generated after multiple iterations, for example,based on the example techniques described with respect to Equations (1)and (2), the vector representation of user query q can be regarded ascomplete and sufficient. In some implementations, only a reversepropagation is needed to generate the vector representation of thengrams using the user queries q_(i), i=1, 2, . . . , N. For example, asshown in FIG. 2, the ngram₃ 214 c can be represented using user query q₁202 a and user query q₂ 202 b.

In some implementations, a user query can be represented by the ngramsin set G with respective weights. The weights corresponding to thengrams can be calculated, for example, to have the vector representationof query q after using the ngram as close as possible to the vectorrepresentation of query q using the word elements in the vocabulary W.In some implementations, the weights corresponding to the ngrams can becalculated to minimize the error between these two values, for example,according to the following equation:

$\begin{matrix}{\min\limits_{W}{\sum_{i = 1}^{n}{{q_{i} - {\sum_{u_{j} \in G_{q_{i}}}{w_{j} \cdot u_{j}}}}}_{2}^{2}}} & {{eq}.\mspace{14mu} 3}\end{matrix}$

where u_(j) represents the vector representation of the ngram (in termsof word elements in the vocabulary W), j=1, . . . , g, w_(j) is theweight corresponding to u_(j), q_(i) represents the vectorrepresentation of the query after iteration, G_(q) _(i) represents theset of ngrams in the query q_(j). In some implementations, the ngramweight w_(j)ϵW in the above equation can be iteratively calculated, forexample, using the gradient descent method or another method. As shownin FIG. 2, query q₁ can be represented using ngram₁, ngram₂, and ngram₃with respective weights w₁, w₂ and w₃; query q₂ can be represented usingngram₃ and ngram₄ with respective weights w₃ and w₄.

FIG. 3 is a flow chart illustrating an example process 300 ofcalculating a score of a user query q and a candidate knowledge pointtopic d using a trained VPCG model, according to an implementation ofthe present disclosure. In some implementations, various steps of method300 can be run in parallel, in combination, in loops, or in any order.For clarity of presentation, the description that follows generallydescribes method 300 in the context of the other figures in thisdescription. However, it will be understood that method 300 may beperformed, for example, by any suitable system, environment, software,and hardware, or a combination of systems, environments, software, andhardware, as appropriate. For example, the method 300 can be performedby a score engine or another data processing apparatus, which caninclude or be implemented by one or more processors.

At 302, a user's query and a number of candidate topics are received.The user query can be a new user query received from a Q&A system oranother intelligent customer service platform. The number of candidatetopics can be, for example, a full set of candidate topics used for userquery matching, a pre-selected set of candidate topics (e.g., based oncoarse or preliminary selection), or another predetermined number ofcandidate topics. In some implementations, the number of candidatetopics can be from the trained VPCG model that includes trained vectorrepresentations of topics based on user click data. After 302, process300 proceeds to 304.

At 304, the user's query can be pre-processed. For example, the querycan be divided into segments, and irrelevant and meaningless words areremoved from each segment. After 304, process 300 proceeds to 306.

At 306, it is determined whether the user's query matches a trained userquery that has been trained by the user click data and stored in theVPCG model. If it is determined that the query matches a trained userquery that has been iteratively calculated in the VPCG model (e.g.,according to the techniques described with respect to FIG. 2), a trainedvector representation of the user query can be obtained directly, forexample, by retrieving the trained vector representation from a datastore, and method 300 proceeds to 312. Otherwise, process 300 proceedsto 308.

At 308, in response to determining that the user query does not matchany trained user query in the VPCG model, an ngram representation of theuser query can be obtained, for example, by the forward maximum matchingalgorithm or another algorithm. In other words, the user query isrepresented by one or more ngrams in an ngram set G_(q) _(i) trainedbased on the user queries in the user click data (e.g., the ngramdictionary G as described with respect to FIG. 2). That is, q: u_(j),u_(j)ϵG_(q) _(i) . In some implementations, the user query can beexpressed using the vector representation in terms of the one or morengrams, for example, as a weighted sum based on the following equation:

q=Σ _(u) _(j) _(ϵG) _(qi) w _(j) ·u _(j)  eq. 4

where u_(j) represents a vector representation of an ngram, j=1, . . . ,g; g represents the size of the ngram set G_(q) _(i) , w_(j) representsthe weight corresponding to ngram u_(j); q_(i) represents a vectorrepresentation of the user query i. After 308, process 300 proceeds to310.

At 310, a vector representation of the query is determined based on thengram representation of the user query. For example, the vectorrepresentation of the query can be determined based on vectorrepresentations and respective weights of the ngrams used in the ngramrepresentation of the user query. As shown in the example with respectto Equation (4), the vector representation of the user query q_(i) usingword elements in the vocabulary is obtained by converting the user queryq_(i) into a weighted sum of ngrams {u_(j)}, where each u_(j) is in itsrespective vector representation using word elements in the vocabulary.After 310, process 300 proceeds to 312.

At 312, a vector representation of each of the number of candidatetopics is obtained. In some implementations, the vector representationof each of the number of candidate topics can be retrieved from thetrained VPCG model that includes trained vector representations oftopics based on user click data. In some implementations, the vectorrepresentation of each of the number of candidate topics can be obtainedin another manner such as an ngram representation similar to thetechniques described above with respect to the user query. After 312,process 300 proceeds to 314.

At 314, a similarity score of the vector representation of the query andthe candidate topic is determined. By converting the user query and eachof the candidate topics into respective vector representation, the userquery can be compared with each of the candidate topics efficiently, forexample, by correlation or computing a distance or error metric of thetwo based on their respective vector representations. The distance orerror metric between a pair of the user query and each of the candidatetopics can be returned as the similarity score that is used to determinethe similarity or match of the user query with each of the candidatetopics. In some implementations, a cosine distance between the vectorrepresentations of the user query and a candidate topic is calculated asan example of the similarity score that is outputted. After 314, process300 ends.

FIG. 4 is a block diagram illustrating an example system 400 providingintelligent customer services based on a VPCG model, according to animplementation of the present disclosure. The system 400 includes asearch engine 404, a refined ranking engine 406 and a re-rank engine412. The system 400 can receive user query 402 and output apredetermined number of topics (e.g., top 3 topics 414 or 416) that aremost relevant to the user query 402, for example, based on thesimilarity scores. The system 400 also uses a VPCG model 408 that istrained based on user click data 410 for determining the top 3 topics416, for example, based on re-ranking performed by the re-rank engine412 based on the similarity scores output from the example process 300.

The input user query 402 can text-based. A user can make a query inwritten or orally, which can be subsequently converted to text. Afterthe system 400 received the user query 402, the search engine 404 (e.g.,Apache Lucene search system) can recall all topics stored in the systemdatabase to have a coarse ranking (or sorting) of the topics based onthe relevancy of each of the topics with respect to the user query 402.In some implementations, the search engine can retrieve, for example, apredetermined number of topics (e.g., the top 400 topics 405) to feedinto the refined ranking engine 406. Within the search engine 404,topics that are irrelevant to the user query 402 can be filtered out.

The recalled top 400 topics 405 can serve as candidate topics forrefined ranking by the refined ranking engine 406. Features associatedwith the user query 402 and the topics can be extracted. Ranking scoresassociated with these features, such as a Word Mover's Distance (WMD)score, Second Sort score, intention tree score, etc., can be calculated.The calculated ranking scores can be sorted by the refined rankingengine 406 using, for example, a LamdaMart model. If a number of thetop-ranked topics satisfy specific criteria (e.g., the scores aresufficiently high or beyond a pre-determined confidence threshold) thatindicates good matches have been found, the number of the top rankedtopics (e.g., the top 3 topics 414) can be output, for example, to theuser via a user interface (UI) as the result of the refined ranking.Otherwise, the refined ranking engine 406 can call the VPCG model 408 tofurther calculate a similarity score 409 of the user query and each ofthe candidate topics.

The re-rank engine 412 can receive the similarity scores 409 calculatedbased on the VPCG model 408, for example, according to the techniquesdescribed with respect to FIG. 3. The re-rank engine 412 can re-rank thecandidate topics based on the similarity scores 409 as well as otherranking scores based on a ranking model, for example, a GradientBoosting Decision Tree (GBDT) model such as LamdaMART. As a result, anumber of the top-ranked topics (e.g., the top 3 topics 416) can beoutput, for example, to the user via a user interface (UI) as the resultof the re-ranking. In some implementations, the user click data 410 canbe used to train the ranking model as well, for example, to determinethe importance of certain ranking features based on Gini coefficients orother criteria. In some implementations, the user click data 410includes about 1.5 million click logs from the user. The VPCG model 408and re-rank engine 412 can use the user click data 410 in theirrespective training processes.

FIG. 5 is a flowchart illustrating an example method 500 for providingintelligent customer services based on a VPCG model, according to animplementation of the present disclosure. In some implementations,various steps of method 500 can be run in parallel, in combination, inloops, or in any order. For clarity of presentation, the descriptionthat follows generally describes method 500 in the context of the otherfigures in this description. However, it will be understood that method500 may be performed, for example, by any suitable system, environment,software, and hardware, or a combination of systems, environments,software, and hardware, as appropriate. For example, the method 500 canbe performed by a data service engine or another data processingapparatus, which can include or be implemented by one or moreprocessors. The example system 400 is an example of the data serviceengine.

At 502, a query is received at a data service engine from a user. Thequery can include a string of characters, for example, a series ofcharacters or word elements. The query can be received, for example,from a user interface (UI) of a Q&A system or another intelligentcustomer service platform. A user can make a text-based query or averbal query, and in the latter case, the verbal query can be convertedto text before it is received at the data service engine. After 502,process 500 proceeds to 504.

At 504, a number of candidate topics are identified based on the queryby the data service engine. Identifying a number of candidate topics caninclude, for example, obtaining a number of candidate topics from amemory or another data store (e.g., a trained VPCG model that includestrained vector representation of topics based on user click data),retrieving a number of candidate topics from another source, receivingthe candidate topics from a device, or otherwise determining thecandidate topics. After 504, process 500 proceeds to 506.

At 506, a first ranking is performed to select one or more topics. Insome implementations, the first rank can be a coarse ranking (orsorting) or preliminary selection of the topics based on the relevancyof each of the topics with respect to the query, for example, accordingto techniques described with respect to FIG. 4. After 506, process 500proceeds to 508.

At 508, it is determined whether the one or more topics meet a specifiedcriterion. In response to determining that the one or more topics meet aspecified criterion (e.g., whether the one or more topics aresufficiently relevant to the query based on certain metrics such asscores and confidence thresholds according to the techniques describedwith respect to FIG. 4), the one or more topics can be output, forexample, to the user via a user interface (UI) of an intelligentcustomer service platform, and method 500 proceeds to 516. Otherwise, ifit is determined that no topic meets the specified criterion, a secondranking (e.g., a refined ranking according to the techniques describedwith respect to FIG. 4) to select one or more topics will be performed,and method 500 proceeds to 510.

At 510, a similarity score between the query and each of the number ofcandidate topics is determined based on a Vector Propagation On a ClickGraph (VPCG) model trained based on user click data. In someimplementations, the user click data comprise a plurality of userselections in response to a plurality of queries from a plurality ofusers, wherein each of the plurality of user selections comprises arespective user's selection of a relevant topic to the user's queryamong a plurality of candidate topics provided to the user based on theuser's query.

Example techniques of training the VPCG model are described with respectto FIG. 2. In some implementations, training the VPCG model includesidentifying a plurality of queries from a plurality of users in the userclick data; identifying a plurality of user selections in response tothe plurality of queries from the plurality of users in the user data;determining a vector representation of each of the plurality of queriesin terms of a plurality of word elements in a vocabulary; anddetermining a vector representation of each of the plurality ofcandidate topics in terms of the plurality of word elements in thevocabulary.

In some implementations, the determining a vector representation of eachof the plurality of queries in terms of a plurality of word elements ina vocabulary comprises representing the each of the plurality of queriesusing the plurality of candidate topics. For example, the representingthe each of the plurality of queries using the plurality of candidatetopics comprises representing each of the plurality of queries accordingto Equation (2).

In some implementations, the determining a vector representation of eachof the plurality of candidate topics in terms of the plurality of wordelements in the vocabulary comprises representing the each of theplurality of candidate topics using the plurality of queries. Forexample, the representing the each of the plurality of candidate topicsusing the plurality of queries comprises representing each of theplurality of candidate topics by a weighted sum of the plurality ofqueries according to Equation (1).

In some implementations, determining a similarity score includes, forexample, converting the query and each of the candidate topics intorespective vector representation, computing a distance or error metricof the two based on their respective vector representations, andreturning the distance or error metric between a pair of the user queryand each of the candidate topics as the similarity score. In someimplementations, a cosine distance between the vector representations ofthe user query and a candidate topic is calculated as an example of thesimilarity score that is outputted.

For example, determining a similarity score between the query and eachof the plurality of candidate topics based on a VPCG model trained basedon user click data comprises determining a vector representation of thequery from the user in terms of the plurality of word elements in thevocabulary; determining a vector representation of each of the pluralityof candidate topics in terms of the plurality of word elements in thevocabulary; calculating the similarity score between a vectorrepresentation of the query and the vector representation of each of theplurality of candidate topics.

In some implementations, determining a vector representation of thequery from the user in terms of the plurality of word elements in thevocabulary comprising representing the query from the user based on aset of ngrams trained based on the user click data. In someimplementations, the set of ngrams comprises a vector representation ofeach ngram in the set of ngrams in terms of the plurality of wordelements in the vocabulary based on the vector representation of each ofthe plurality of queries in the user click data.

In some implementations, representing the query from the user based on aset of ngrams trained based on the user click data comprisesrepresenting the query from the user as a weighted sum the set ofngrams, according to Equation (4). After 510, process 500 proceeds to512.

At 512, the number of candidate topics are ranked based on thesimilarity scores. In some implementations, the data service engine canreceive the similarity score determined at 510, and rank or re-rank thecandidate topics based on the similarity scores. In someimplementations, the data service engine will also use other rankingscores based on a ranking model, for example, a GDBP model when rankingthe candidate topics, for example, according to the techniques describedwith respect to FIG. 4. After 512, process 500 proceeds to 514.

At 514, one or more topics are selected from the ranked candidatetopics. In some implementations, for example, a number of top-rankedtopics (e.g., the top 3 topics) can be selected from the rankedcandidate topics. After 514, process 500 proceeds to 516.

At 516, the selected topics are output via a user interface (UI) of thedata service engine. In some implementations, the selected topics can bedisplayed in a table, a chat box, a pop window, etc. in a graphic userinterface (GUI) or other UI. After 516, process 500 stops.

FIG. 6 is a plot illustrating a performance comparison betweenintelligent customer services based on a VPCG model and a Deep SemanticSimilarity Model (DSSM), according to an implementation of the presentdisclosure. As illustrated in FIG. 6, the VPCG model's score plug-in isbased on C++ code-based model server deployment, with an average calldelay of about 1 ms, while the DSSM takes nearly 70 ms, which shows thatthe VPCG model greatly improves the efficiency of online operations andreduces the algorithm latency. Given more candidate topics, theVPCG-model-based implementation can achieve more time saving. With theintroduction of the re-ranker engine based on the VPCG model, the resultof the LambdaMart sorting improves the ndcg@1 indicator by 2%, and theon-line test single-engine resolution rate increases by about 1.5%.

Table 1 shows several example vector representations of user queries.Under the “Vector Representation” column, each word or phrase (e.g.,“fund” or “transfer out”) preceding the colon (“:”) represents a workelement in the vocabulary. The value (e.g., “0.83” and “0.06”) followingthe colon (“:”) represents a weight corresponding to the word element.As shown in Table 1, based on the VPCG model, the content of a userquery can be represented by the word elements more comprehensively, evenby word elements that do not appear in the user query. The VPCG modelcan also learn various vector representations through machine learning.For example, the vector representation of the user query “How to redeemthe fund,” includes word elements “sell,” “transfer,” “sale,” etc.,fully expressing the semantics of the user query. Such a representationcan better match the user's needs or intents, and thus provide moreappropriate or effective answer to the user's query.

TABLE 1 Example Vector Representations of User Queries based on a VPCGModel User queries Vector representations How to redeem fund:0.83how:0.36 sell:0.21 redeem:0.18 the fund how:0.18 money:0.14 out:0.06transfer out:0.06 sale:0.05 can:0.05 transfer:0.05 Hwo to check Bankcard:0.62 transaction:0.41 how:0.26 all the bank check:0.22 bill :0.22search:0.16 how is it:0.14 transaction Alipay:0.13 record:0.11 all:0.08money:0.08 view:0.08 card number:0.08 balance:0.06 Order paymentOrder:0.36 not:0.36 arrive:0.33 payment:0.31 has not no:0.30 how:0.26moeny:0.22 succeed:0.22 arrived yet yet:0.21 pay:0.20 show:0.16transaction:0.14

FIG. 7 is a block diagram of an example computer system 700 used toprovide computational functionalities associated with describedalgorithms, methods, functions, processes, flows, and procedures, asdescribed in the instant disclosure, according to an implementation. Theillustrated computer 702 is intended to encompass any computing devicesuch as a server, desktop computer, laptop/notebook computer, wirelessdata port, smart phone, personal data assistant (PDA), tablet computingdevice, one or more processors within these devices, or any othersuitable processing device, including physical or virtual instances (orboth) of the computing device. Additionally, the computer 702 maycomprise a computer that includes an input device, such as a keypad,keyboard, touch screen, or other device that can accept userinformation, and an output device that conveys information associatedwith the operation of the computer 702, including digital data, visual,or audio information (or a combination of information), or agraphical-type user interface (UI) (or GUI).

The computer 702 can serve in a role as a client, network component, aserver, a database or other persistency, or any other component (or acombination of roles) of a computer system for performing the subjectmatter described in the instant disclosure. The illustrated computer 702is communicably coupled with a network 730. In some implementations, oneor more components of the computer 702 may be configured to operatewithin environments, including cloud-computing-based, local, global, orother environment (or a combination of environments).

At a high level, the computer 702 is an electronic computing deviceoperable to receive, transmit, process, store, or manage data andinformation associated with the described subject matter. According tosome implementations, the computer 702 may also include or becommunicably coupled with an application server, e-mail server, webserver, caching server, streaming data server, or other server (or acombination of servers).

The computer 702 can receive requests over network 730 from a clientapplication (for example, executing on another computer 702) and respondto the received requests by processing the received requests using anappropriate software application(s). In addition, requests may also besent to the computer 702 from internal users (for example, from acommand console or by other appropriate access method), external orthird-parties, other automated applications, as well as any otherappropriate entities, individuals, systems, or computers.

Each of the components of the computer 702 can communicate using asystem bus 703. In some implementations, any or all of the components ofthe computer 702, hardware or software (or a combination of bothhardware and software), may interface with each other or the interface704 (or a combination of both), over the system bus 703 using anapplication programming interface (API) 712 or a service layer 713 (or acombination of the API 712 and service layer 713). The API 712 mayinclude specifications for routines, data structures, and objectclasses. The API 712 may be either computer-language independent ordependent and refer to a complete interface, a single function, or evena set of APIs. The service layer 713 provides software services to thecomputer 702 or other components (whether or not illustrated) that arecommunicably coupled to the computer 702. The functionality of thecomputer 702 may be accessible for all service consumers using thisservice layer. Software services, such as those provided by the servicelayer 713, provide reusable, defined functionalities through a definedinterface. For example, the interface may be software written in JAVA,C++, or other suitable language providing data in extensible markuplanguage (XML) format or other suitable format. While illustrated as anintegrated component of the computer 702, alternative implementationsmay illustrate the API 712 or the service layer 713 as stand-alonecomponents in relation to other components of the computer 702 or othercomponents (whether or not illustrated) that are communicably coupled tothe computer 702. Moreover, any or all parts of the API 712 or theservice layer 713 may be implemented as child or sub-modules of anothersoftware module, enterprise application, or hardware module withoutdeparting from the scope of this disclosure.

The computer 702 includes an interface 704. Although illustrated as asingle interface 704 in FIG. 7, two or more interfaces 704 may be usedaccording to particular needs, desires, or particular implementations ofthe computer 702. The interface 704 is used by the computer 702 forcommunicating with other systems that are connected to the network 730(whether illustrated or not) in a distributed environment. Generally,the interface 704 comprises logic encoded in software or hardware (or acombination of software and hardware) and is operable to communicatewith the network 730. More specifically, the interface 704 may comprisesoftware supporting one or more communication protocols associated withcommunications such that the network 730 or interface's hardware isoperable to communicate physical signals within and outside of theillustrated computer 702.

The computer 702 includes a processor 705. Although illustrated as asingle processor 705 in FIG. 7, two or more processors may be usedaccording to particular needs, desires, or particular implementations ofthe computer 702. Generally, the processor 705 executes instructions andmanipulates data to perform the operations of the computer 702 and anyalgorithms, methods, functions, processes, flows, and procedures asdescribed in the instant disclosure.

The computer 702 also includes a database 706 that can hold data for thecomputer 702 or other components (or a combination of both) that can beconnected to the network 730 (whether illustrated or not). For example,database 706 can be an in-memory, conventional, or other type ofdatabase storing data consistent with this disclosure. In someimplementations, database 706 can be a combination of two or moredifferent database types (for example, a hybrid in-memory andconventional database) according to particular needs, desires, orparticular implementations of the computer 702 and the describedfunctionality. Although illustrated as a single database 706 in FIG. 7,two or more databases (of the same or combination of types) can be usedaccording to particular needs, desires, or particular implementations ofthe computer 702 and the described functionality. While database 706 isillustrated as an integral component of the computer 702, in alternativeimplementations, database 706 can be external to the computer 702. Asillustrated, the database 706 holds one or more vector representationsdata 716 which can include vector representation of user queries,candidate topics, ngrams, etc., VPCG model data 718, and click data 726.

The computer 702 also includes a memory 707 that can hold data for thecomputer 702 or other components (or a combination of both) that can beconnected to the network 730 (whether illustrated or not). Memory 707can store any data consistent with this disclosure. In someimplementations, memory 707 can be a combination of two or moredifferent types of memory (for example, a combination of semiconductorand magnetic storage) according to particular needs, desires, orparticular implementations of the computer 702 and the describedfunctionality. Although illustrated as a single memory 707 in FIG. 7,two or more memories 707 (of the same or combination of types) can beused according to particular needs, desires, or particularimplementations of the computer 702 and the described functionality.While memory 707 is illustrated as an integral component of the computer702, in alternative implementations, memory 707 can be external to thecomputer 702.

The application 708 is an algorithmic software engine providingfunctionality according to particular needs, desires, or particularimplementations of the computer 702, particularly with respect tofunctionality described in this disclosure. For example, application 708can serve as one or more components, modules, or applications. Further,although illustrated as a single application 708, the application 708may be implemented as multiple applications 708 on the computer 702. Inaddition, although illustrated as integral to the computer 702, inalternative implementations, the application 708 can be external to thecomputer 702.

The computer 702 can also include a power supply 714. The power supply714 can include a rechargeable or non-rechargeable battery that can beconfigured to be either user- or non-user-replaceable. In someimplementations, the power supply 714 can include power-conversion ormanagement circuits (including recharging, standby, or other powermanagement functionality). In some implementations, the power-supply 714can include a power plug to allow the computer 702 to be plugged into awall socket or other power source to, for example, power the computer702 or recharge a rechargeable battery.

There may be any number of computers 702 associated with, or externalto, a computer system containing computer 702, each computer 702communicating over network 730. Further, the term “client,” “user,” andother appropriate terminology may be used interchangeably, asappropriate, without departing from the scope of this disclosure.Moreover, this disclosure contemplates that many users may use onecomputer 702, or that one user may use multiple computers 702.

Described implementations of the subject matter can include one or morefeatures, alone or in combination.

For example, in a first implementation, a computer-implemented methodcomprising: receiving, at a data service engine, a query from a user,wherein the query comprises a string of characters; identifying, by thedata service engine, multiple candidate topics based on the query;determining a similarity score between the query and each of themultiple candidate topics based on a Vector Propagation On a Click Graph(VPCG) model trained based on user click data; ranking the multiplecandidate topics based on the similarity scores; selecting one or moretopics from the ranked candidate topics; and outputting the selectedtopics via a user interface (UI).

The foregoing and other described implementations can each, optionally,include one or more of the following features:

A first feature, combinable with any of the following features, furthercomprising, before determining a similarity score between the query andeach candidate topic based on a Vector Propagation On a Click Graph(VPCG) model trained based on user click data, performing a firstranking to select a second one or more topics from the plurality ofcandidate topics. In response to determining that each of the second oneor more topics does not meet a specified criterion, performing a secondranking based on the VPCG model trained based on user click data,wherein performing the second ranking comprises the determining asimilarity score between the query and each candidate topic based on aVPCG model trained based on user click data; and the ranking theplurality of candidate topics based on the similarity scores.

A second feature, combinable with any of the previous or followingfeatures, wherein the user click data comprise a plurality of userselections in response to a plurality of queries from a plurality ofusers, wherein each of the plurality of user selections comprises arespective user's selection of a relevant topic to the user's queryamong a plurality of candidate topics provided to the user based on theuser's query.

A third feature, combinable with any of the previous or followingfeatures, further comprising training the VPCG model based on the userclick data, wherein the training the VPCG model comprises, identifying aplurality of queries from a plurality of users in the user click data;identifying a plurality of user selections in response to the pluralityof queries from the plurality of users in the user click data;determining a vector representation of each of the plurality of queriesin terms of a plurality of word elements in a vocabulary; anddetermining a vector representation of each of the plurality ofcandidate topics in terms of the plurality of word elements in thevocabulary.

A fourth feature, combinable with any of the previous or followingfeatures, wherein, the determining a vector representation of each ofthe multiple queries in terms of a number of word elements in avocabulary comprises representing the each of a number of queries usingthe plurality of candidate topics; and the determining a vectorrepresentation of each of the multiple of candidate topics in terms of anumber of word elements in the vocabulary comprises representing theeach of a number of candidate topics using the plurality of queries.

A fifth feature, combinable with any of the previous or followingfeatures, wherein the representing the each of the multiple candidatetopics using the multiple queries comprises representing each of themultiple candidate topics by a weighted sum of the multiple queriesaccording to equation:

$d_{j}^{(n)} = {\frac{1}{{{\sum_{i = 1}^{Q}{C_{i,j} \cdot q_{i}^{({n - 1})}}}}_{2}}{\sum_{i = 1}^{Q}{C_{i,j} \cdot q_{i}^{({n - 1})}}}}$

and the representing the each of the plurality of queries using theplurality of candidate topics comprises representing each of theplurality of queries according to equation:

$q_{i}^{(n)} = {\frac{1}{{{\sum_{j = 1}^{D}{C_{i,j} \cdot q_{j}^{({n - 1})}}}}_{2}}{\sum_{j = 1}^{D}{C_{i,j} \cdot d_{j}^{(n)}}}}$

wherein d_(j) ^((n)) represents a vector representation of topic d_(j)in the (n)th interation; q_(i) ^((n-1)) represents a vectorrepresentation of query q_(i) in the (n−1) th interation; C_(i,j)represents a total number of user clicks or selections of the topicd_(j) for the query q_(i) in the user click data; Q represents the totalnumber of user queries in the user click data, i.e., q_(i), i=1, 2, . .. , Q. D represents the total number of titles in the user click data,i.e., d_(j), j=1, 2, . . . , D.

A sixth feature, combinable with any of the previous or followingfeatures, wherein the determining a similarity score between the queryand each of the multiple candidate topics based on a VPCG model trainedbased on user click data comprises, determining a vector representationof the query from the user in terms of the multiple word elements in thevocabulary; determining a vector representation of each of the multiplecandidate topics in terms of the plurality of word elements in thevocabulary; and calculating the similarity score between a vectorrepresentation of the query and the vector representation of each of themultiple candidate topics.

A seventh feature, combinable with any of the previous or followingfeatures, wherein the determining a vector representation of the queryfrom the user in terms of the plurality of word elements in thevocabulary comprising representing the query from the user based on aset of ngrams trained based on the user click data.

A eighth feature, combinable with any of the previous or followingfeatures, wherein the set of ngrams comprises a vector representation ofeach ngram in the set of ngrams in terms of the multiple word elementsin the vocabulary based on the vector representation of each of themultiple queries in the user click data.

A ninth feature, combinable with any of the previous or followingfeatures, wherein the representing the query from the user based on aset of ngrams trained based on the user click data comprisesrepresenting the query from the user as a weighted sum the set ofngrams, according to equation:

$q = {\sum\limits_{u_{j} \in G_{q_{i}}}\; {w_{j} \cdot u_{j}}}$

wherein u_(j) represents a vector representation of ngram j, j=1, . . ., g; g represents a total number of ngrams in the set of the ngrams;w_(j) represents a weight corresponding to u_(j); G_(q) _(i) the set ofthe ngrams trained based on the user click data.

In a second implementation, a computer-implemented system comprising:one or more computers; and one or more computer memory devicesinteroperably coupled with the one or more computers and havingtangible, non-transitory, machine-readable media storing instructions,that when executed by the one or more computers, perform operationscomprising: receiving, at a data service engine, a query from a user,wherein the query comprises a string of characters; identifying, by thedata service engine, multiple candidate topics based on the query;determining a similarity score between the query and each of themultiple candidate topics based on a Vector Propagation On a Click Graph(VPCG) model trained based on user click data; ranking the multiplecandidate topics based on the similarity scores; selecting one or moretopics from the ranked candidate topics; and outputting the selectedtopics via a user interface (UI).

The foregoing and other described implementations can each, optionally,include one or more of the following features:

A first feature, combinable with any of the following features, theoperations further comprising, before determining a similarity scorebetween the query and each candidate topic based on a Vector PropagationOn a Click Graph (VPCG) model trained based on user click data,performing a first ranking to select a second one or more topics fromthe plurality of candidate topics; and in response to determining thateach of the second one or more topics does not meet a specifiedcriterion, performing a second ranking based on the VPCG model trainedbased on user click data, wherein performing the second rankingcomprises: the determining a similarity score between the query and eachcandidate topic based on a VPCG model trained based on user click data;and the ranking the plurality of candidate topics based on thesimilarity scores.

A second feature, combinable with any of the previous or followingfeatures, wherein the user click data comprise a plurality of userselections in response to a plurality of queries from a plurality ofusers, wherein each of the plurality of user selections comprises arespective user's selection of a relevant topic to the user's queryamong a plurality of candidate topics provided to the user based on theuser's query.

A third feature, combinable with any of the previous or followingfeatures, the operations further comprising training the VPCG modelbased on the user click data, wherein the training the VPCG modelcomprises, identifying a plurality of queries from a plurality of usersin the user click data; identifying a plurality of user selections inresponse to the plurality of queries from the plurality of users in theuser data; determining a vector representation of each of the pluralityof queries in terms of a plurality of word elements in a vocabulary; anddetermining a vector representation of each of the plurality ofcandidate topics in terms of the plurality of word elements in thevocabulary.

A fourth feature, combinable with any of the previous or followingfeatures, wherein, the determining a vector representation of each ofthe multiple queries in terms of a number of word elements in avocabulary comprises representing the each of a number of queries usingthe plurality of candidate topics; and the determining a vectorrepresentation of each of the multiple of candidate topics in terms of anumber of word elements in the vocabulary comprises representing theeach of a number of candidate topics using the plurality of queries.

A fifth feature, combinable with any of the previous or followingfeatures, wherein the representing the each of the multiple candidatetopics using the multiple queries comprises representing each of themultiple candidate topics by a weighted sum of the multiple queriesaccording to equation:

${d_{j}^{(n)} = {\frac{1}{{{\sum_{i = 1}^{Q}{C_{i,j} \cdot q_{i}^{({n - 1})}}}}_{2}}{\sum_{i = 1}^{Q}{C_{i,j} \cdot q_{i}^{({n - 1})}}}}};$

and the representing the each of the plurality of queries using theplurality of candidate topics comprises representing each of theplurality of queries according to equation:

$q_{i}^{(n)} = {\frac{1}{{{\sum_{j = 1}^{D}{C_{i,j} \cdot q_{j}^{({n - 1})}}}}_{2}}{\sum_{j = 1}^{D}{C_{i,j} \cdot d_{j}^{(n)}}}}$

wherein d_(j) ^((n)) represents a vector representation of topic d_(j)in the (n)th interation; q_(i) ^((n-1)) represents a vectorrepresentation of query q_(i) in the (n−1) th interation; C_(i,j)represents a total number of user clicks or selections of the topicd_(j) for the query q_(i) in the user click data; Q represents a totalnumber of user queries in the user click data, i.e., q_(i), i=1, 2, . .. , Q; D represents a total number of topics in the user click data,i.e., d_(j), j=1, 2, . . . , D.

A sixth feature, combinable with any of the previous or followingfeatures, wherein the determining a similarity score between the queryand each of the multiple candidate topics based on a VPCG model trainedbased on user click data comprises, determining a vector representationof the query from the user in terms of the multiple word elements in thevocabulary; determining a vector representation of each of the multiplecandidate topics in terms of the plurality of word elements in thevocabulary; and calculating the similarity score between a vectorrepresentation of the query and the vector representation of each of themultiple candidate topics.

A seventh feature, combinable with any of the previous or followingfeatures, wherein the determining a vector representation of the queryfrom the user in terms of the plurality of word elements in thevocabulary comprising representing the query from the user based on aset of ngrams trained based on the user click data.

A eighth feature, combinable with any of the previous or followingfeatures, wherein the set of ngrams comprises a vector representation ofeach ngram in the set of ngrams in terms of the multiple word elementsin the vocabulary based on the vector representation of each of themultiple queries in the user click data.

A ninth feature, combinable with any of the previous or followingfeatures, wherein the representing the query from the user based on aset of ngrams trained based on the user click data comprisesrepresenting the query from the user as a weighted sum the set ofngrams, according to equation:

$q = {\sum\limits_{u_{j} \in G_{q_{i}}}\; {w_{j} \cdot u_{j}}}$

wherein u_(j) represents a vector representation of ngram j, j=1, . . ., g; g represents a total number of ngrams in the set of the ngrams;w_(j) represents a weight corresponding to u_(j); G_(q) _(i) the set ofthe ngrams trained based on the user click data.

In a third implementation, a non-transitory, computer-readable mediumstoring one or more instructions executable by a computer-implementedsystem to perform operations comprising: receiving, at a data serviceengine, a query from a user, wherein the query comprises a string ofcharacters; identifying, by the data service engine, multiple candidatetopics based on the query; determining a similarity score between thequery and each of the multiple candidate topics based on a VectorPropagation On a Click Graph (VPCG) model trained based on user clickdata; ranking the multiple candidate topics based on the similarityscores; selecting one or more topics from the ranked candidate topics;and outputting the selected topics via a user interface (UI).

The foregoing and other described implementations can each, optionally,include one or more of the following features:

A first feature, combinable with any of the following features, theoperations further comprising, before determining a similarity scorebetween the query and each candidate topic based on a Vector PropagationOn a Click Graph (VPCG) model trained based on user click data,performing a first ranking to select a second one or more topics fromthe plurality of candidate topics; and in response to determining thateach of the second one or more topics does not meet a specifiedcriterion, performing a second ranking based on the VPCG model trainedbased on user click data, wherein performing the second rankingcomprises: the determining a similarity score between the query and eachcandidate topic based on a VPCG model trained based on user click data;and the ranking the plurality of candidate topics based on thesimilarity scores.

A second feature, combinable with any of the previous or followingfeatures, wherein the user click data comprise a plurality of userselections in response to a plurality of queries from a plurality ofusers, wherein each of the plurality of user selections comprises arespective user's selection of a relevant topic to the user's queryamong a plurality of candidate topics provided to the user based on theuser's query.

A third feature, combinable with any of the previous or followingfeatures, the operations further comprising training the VPCG modelbased on the user click data, wherein the training the VPCG modelcomprises, identifying a plurality of queries from a plurality of usersin the user click data; identifying a plurality of user selections inresponse to the plurality of queries from the plurality of users in theuser data; determining a vector representation of each of the pluralityof queries in terms of a plurality of word elements in a vocabulary; anddetermining a vector representation of each of the plurality ofcandidate topics in terms of the plurality of word elements in thevocabulary.

A fourth feature, combinable with any of the previous or followingfeatures, wherein, the determining a vector representation of each ofthe multiple queries in terms of a number of word elements in avocabulary comprises representing the each of a number of queries usingthe plurality of candidate topics; and the determining a vectorrepresentation of each of the multiple of candidate topics in terms of anumber of word elements in the vocabulary comprises representing theeach of a number of candidate topics using the plurality of queries.

A fifth feature, combinable with any of the previous or followingfeatures, wherein the representing the each of the multiple candidatetopics using the multiple queries comprises representing each of themultiple candidate topics by a weighted sum of the multiple queriesaccording to equation:

${d_{j}^{(n)} = {\frac{1}{{{\sum_{i = 1}^{Q}{C_{i,j} \cdot q_{i}^{({n - 1})}}}}_{2}}{\sum_{i = 1}^{Q}{C_{i,j} \cdot q_{i}^{({n - 1})}}}}};$

and the representing the each of the plurality of queries using theplurality of candidate topics comprises representing each of theplurality of queries according to equation:

$q_{i}^{(n)} = {\frac{1}{{{\sum_{j = 1}^{D}{C_{i,j} \cdot q_{j}^{({n - 1})}}}}_{2}}{\sum_{j = 1}^{D}{C_{i,j} \cdot d_{j}^{(n)}}}}$

wherein d_(j) ^((n)) represents a vector representation of topic d_(j)in the (n)th interation; q_(i) ^((n-1)) represents a vectorrepresentation of query q_(i) in the (n−1) th interation; C_(i,j)represents a total number of user clicks or selections of the topicd_(j) for the query q_(i) in the user click data; Q represents the totalnumber of user queries in the user click data, i.e., q_(i), i=1, 2, . .. , Q; D represents the total number of topics in the user click data,i.e., d_(j), j=1, 2, . . . , D.

A sixth feature, combinable with any of the previous or followingfeatures, wherein the determining a similarity score between the queryand each of the multiple candidate topics based on a VPCG model trainedbased on user click data comprises, determining a vector representationof the query from the user in terms of the multiple word elements in thevocabulary; determining a vector representation of each of the multiplecandidate topics in terms of the plurality of word elements in thevocabulary; and calculating the similarity score between a vectorrepresentation of the query and the vector representation of each of themultiple candidate topics.

A seventh feature, combinable with any of the previous or followingfeatures, wherein the determining a vector representation of the queryfrom the user in terms of the plurality of word elements in thevocabulary comprising representing the query from the user based on aset of ngrams trained based on the user click data.

A eighth feature, combinable with any of the previous or followingfeatures, wherein the set of ngrams comprises a vector representation ofeach ngram in the set of ngrams in terms of the multiple word elementsin the vocabulary based on the vector representation of each of themultiple queries in the user click data.

A ninth feature, combinable with any of the previous or followingfeatures, wherein the representing the query from the user based on aset of ngrams trained based on the user click data comprisesrepresenting the query from the user as a weighted sum the set ofngrams, according to equation:

$q = {\sum\limits_{u_{j} \in G_{q_{i}}}\; {w_{j} \cdot u_{j}}}$

wherein u_(j) represents a vector representation of ngram j, j=1, . . ., g; g represents a total number of ngrams in the set of the ngrams;w_(j) represents a weight corresponding to u_(j); G_(q) _(i) the set ofthe ngrams trained based on the user click data.

Implementations of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, in tangibly embodied computer software or firmware, incomputer hardware, including the structures disclosed in this behaviorand their structural equivalents, or in combinations of one or more ofthem. Software implementations of the described subject matter can beimplemented as one or more computer programs, that is, one or moremodules of computer program instructions encoded on a tangible,non-transitory, computer-readable medium for execution by, or to controlthe operation of, a computer or computer-implemented system.Alternatively, or additionally, the program instructions can be encodedin/on an artificially generated propagated signal, for example, amachine-generated electrical, optical, or electromagnetic signal that isgenerated to encode information for transmission to a receiver apparatusfor execution by a computer or computer-implemented system. Thecomputer-storage medium can be a machine-readable storage device, amachine-readable storage substrate, a random or serial access memorydevice, or a combination of computer-storage mediums. Configuring one ormore computers means that the one or more computers have installedhardware, firmware, or software (or combinations of hardware, firmware,and software) so that when the software is executed by the one or morecomputers, particular computing operations are performed.

The term “real-time,” “real time,” “realtime,” “real (fast) time (RFT),”“near(ly) real-time (NRT),” “quasi real-time,” or similar terms (asunderstood by one of ordinary skill in the art), means that an actionand a response are temporally proximate such that an individualperceives the action and the response occurring substantiallysimultaneously. For example, the time difference for a response todisplay (or for an initiation of a display) of data following theindividual's action to access the data can be less than 1 millisecond(ms), less than 1 second (s), or less than 5 s. While the requested dataneed not be displayed (or initiated for display) instantaneously, it isdisplayed (or initiated for display) without any intentional delay,taking into account processing limitations of a described computingsystem and time required to, for example, gather, accurately measure,analyze, process, store, or transmit the data.

The terms “data processing apparatus,” “computer,” or “electroniccomputer device” (or an equivalent term as understood by one of ordinaryskill in the art) refer to data processing hardware. Data processinghardware encompass all kinds of apparatuses, devices, and machines forprocessing data, including by way of example, a programmable processor,a computer, or multiple processors or computers. The computer can alsobe, or further include special purpose logic circuitry, for example, acentral processing unit (CPU), a field programmable gate array (FPGA),or an application-specific integrated circuit (ASIC). In someimplementations, the computer or computer-implemented system or specialpurpose logic circuitry (or a combination of the computer orcomputer-implemented system and special purpose logic circuitry) can behardware- or software-based (or a combination of both hardware- andsoftware-based). The computer can optionally include code that createsan execution environment for computer programs, for example, code thatconstitutes processor firmware, a protocol stack, a database managementsystem, an operating system, or a combination of execution environments.The present disclosure contemplates the use of a computer orcomputer-implemented system with an operating system of some type, forexample LINUX, UNIX, WINDOWS, MAC OS, ANDROID, IOS, another operatingsystem, or a combination of operating systems.

A computer program, which can also be referred to or described as aprogram, software, a software application, a unit, a module, a softwaremodule, a script, code, or other component can be written in any form ofprogramming language, including compiled or interpreted languages, ordeclarative or procedural languages, and it can be deployed in any form,including, for example, as a stand-alone program, module, component, orsubroutine, for use in a computing environment. A computer program can,but need not, correspond to a file in a file system. A program can bestored in a portion of a file that holds other programs or data, forexample, one or more scripts stored in a markup language document, in asingle file dedicated to the program in question, or in multiplecoordinated files, for example, files that store one or more modules,sub-programs, or portions of code. A computer program can be deployed tobe executed on one computer or on multiple computers that are located atone site or distributed across multiple sites and interconnected by acommunication network.

While portions of the programs illustrated in the various figures can beillustrated as individual components, such as units or modules, thatimplement described features and functionality using various objects,methods, or other processes, the programs can instead include a numberof sub-units, sub-modules, third-party services, components, libraries,and other components, as appropriate. Conversely, the features andfunctionality of various components can be combined into singlecomponents, as appropriate. Thresholds used to make computationaldeterminations can be statically, dynamically, or both statically anddynamically determined.

Described methods, processes, or logic flows represent one or moreexamples of functionality consistent with the present disclosure and arenot intended to limit the disclosure to the described or illustratedimplementations, but to be accorded the widest scope consistent withdescribed principles and features. The described methods, processes, orlogic flows can be performed by one or more programmable computersexecuting one or more computer programs to perform functions byoperating on input data and generating output data. The methods,processes, or logic flows can also be performed by, and computers canalso be implemented as, special purpose logic circuitry, for example, aCPU, an FPGA, or an ASIC.

Computers for the execution of a computer program can be based ongeneral or special purpose microprocessors, both, or another type ofCPU. Generally, a CPU will receive instructions and data from and writeto a memory. The essential elements of a computer are a CPU, forperforming or executing instructions, and one or more memory devices forstoring instructions and data. Generally, a computer will also include,or be operatively coupled to, receive data from or transfer data to, orboth, one or more mass storage devices for storing data, for example,magnetic, magneto-optical disks, or optical disks. However, a computerneed not have such devices. Moreover, a computer can be embedded inanother device, for example, a mobile telephone, a personal digitalassistant (PDA), a mobile audio or video player, a game console, aglobal positioning system (GPS) receiver, or a portable memory storagedevice.

Non-transitory computer-readable media for storing computer programinstructions and data can include all forms of permanent/non-permanentor volatile/non-volatile memory, media and memory devices, including byway of example semiconductor memory devices, for example, random accessmemory (RAM), read-only memory (ROM), phase change memory (PRAM), staticrandom access memory (SRAM), dynamic random access memory (DRAM),erasable programmable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), and flash memory devices;magnetic devices, for example, tape, cartridges, cassettes,internal/removable disks; magneto-optical disks; and optical memorydevices, for example, digital versatile/video disc (DVD), compact disc(CD)-ROM, DVD+/−R, DVD-RAM, DVD-ROM, high-definition/density (HD)-DVD,and BLU-RAY/BLU-RAY DISC (BD), and other optical memory technologies.The memory can store various objects or data, including caches, classes,frameworks, applications, modules, backup data, jobs, web pages, webpage templates, data structures, database tables, repositories storingdynamic information, or other appropriate information including anyparameters, variables, algorithms, instructions, rules, constraints, orreferences. Additionally, the memory can include other appropriate data,such as logs, policies, security or access data, or reporting files. Theprocessor and the memory can be supplemented by, or incorporated in,special purpose logic circuitry.

To provide for interaction with a user, implementations of the subjectmatter described in this behavior can be implemented on a computerhaving a display device, for example, a cathode ray tube (CRT), liquidcrystal display (LCD), light emitting diode (LED), or plasma monitor,for displaying information to the user and a keyboard and a pointingdevice, for example, a mouse, trackball, or trackpad by which the usercan provide input to the computer. Input can also be provided to thecomputer using a touchscreen, such as a tablet computer surface withpressure sensitivity, a multi-touch screen using capacitive or electricsensing, or another type of touchscreen. Other types of devices can beused to interact with the user. For example, feedback provided to theuser can be any form of sensory feedback (such as, visual, auditory,tactile, or a combination of feedback types). Input from the user can bereceived in any form, including acoustic, speech, or tactile input. Inaddition, a computer can interact with the user by sending documents toand receiving documents from a client computing device that is used bythe user (for example, by sending web pages to a web browser on a user'smobile computing device in response to requests received from the webbrowser).

The term “graphical user interface,” or “GUI,” can be used in thesingular or the plural to describe one or more graphical user interfacesand each of the displays of a particular graphical user interface.Therefore, a GUI can represent any graphical user interface, includingbut not limited to, a web browser, a touch screen, or a command lineinterface (CLI) that processes information and efficiently presents theinformation results to the user. In general, a GUI can include a numberof user interface (UI) elements, some or all associated with a webbrowser, such as interactive fields, pull-down lists, and buttons. Theseand other UI elements can be related to or represent the functions ofthe web browser.

Implementations of the subject matter described in this behavior can beimplemented in a computing system that includes a back-end component,for example, as a data server, or that includes a middleware component,for example, an application server, or that includes a front-endcomponent, for example, a client computer having a graphical userinterface or a Web browser through which a user can interact with animplementation of the subject matter described in this behavior, or anycombination of one or more such back-end, middleware, or front-endcomponents. The components of the system can be interconnected by anyform or medium of wireline or wireless digital data communication (or acombination of data communication), for example, a communicationnetwork. Examples of communication networks include a local area network(LAN), a radio access network (RAN), a metropolitan area network (MAN),a wide area network (WAN), Worldwide Interoperability for MicrowaveAccess (WIMAX), a wireless local area network (WLAN) using, for example,802.11a/b/g/n or 802.20 (or a combination of 802.11x and 802.20 or otherprotocols consistent with the present disclosure), all or a portion ofthe Internet, another communication network, or a combination ofcommunication networks. The communication network can communicate with,for example, Internet Protocol (IP) packets, frame relay frames,Asynchronous Transfer Mode (ATM) cells, voice, video, data, or otherinformation between network nodes.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this behavior contains many specific implementation details, theseshould not be construed as limitations on the scope of any inventiveconcept or on the scope of what can be claimed, but rather asdescriptions of features that can be specific to particularimplementations of particular inventive concepts. Certain features thatare described in this behavior in the context of separateimplementations can also be implemented, in combination, in a singleimplementation. Conversely, various features that are described in thecontext of a single implementation can also be implemented in multipleimplementations, separately, or in any sub-combination. Moreover,although previously described features can be described as acting incertain combinations and even initially claimed as such, one or morefeatures from a claimed combination can, in some cases, be excised fromthe combination, and the claimed combination can be directed to asub-combination or variation of a sub-combination.

Particular implementations of the subject matter have been described.Other implementations, alterations, and permutations of the describedimplementations are within the scope of the following claims as will beapparent to those skilled in the art. While operations are depicted inthe drawings or claims in a particular order, this should not beunderstood as requiring that such operations be performed in theparticular order shown or in sequential order, or that all illustratedoperations be performed (some operations can be considered optional), toachieve desirable results. In certain circumstances, multitasking orparallel processing (or a combination of multitasking and parallelprocessing) can be advantageous and performed as deemed appropriate.

Moreover, the separation or integration of various system modules andcomponents in the previously described implementations should not beunderstood as requiring such separation or integration in allimplementations, and it should be understood that the described programcomponents and systems can generally be integrated together in a singlesoftware product or packaged into multiple software products.

Accordingly, the previously described example implementations do notdefine or constrain the present disclosure. Other changes,substitutions, and alterations are also possible without departing fromthe spirit and scope of the present disclosure.

Furthermore, any claimed implementation is considered to be applicableto at least a computer-implemented method; a non-transitory,computer-readable medium storing computer-readable instructions toperform the computer-implemented method; and a computer systemcomprising a computer memory interoperably coupled with a hardwareprocessor configured to perform the computer-implemented method or theinstructions stored on the non-transitory, computer-readable medium.

What is claim is:
 1. A computer-implemented method, comprising:receiving, at a data service engine, a query from a user, wherein thequery comprises a string of characters; identifying, by the data serviceengine, a plurality of candidate topics based on the query; identifyinga plurality of queries from a plurality of users in user click data;determining a similarity score between the query and each of theplurality of candidate topics based on a Vector Propagation On a ClickGraph (VPCG) model trained based on the user click data, wherein theVPCG model comprises: a vector representation of each of the pluralityof candidate topics based on a first vector representation of each ofthe plurality of queries; and a second vector representation of each ofthe plurality of queries based on the vector representation of each ofthe plurality of candidate topics; and ranking the plurality ofcandidate topics based on the similarity scores; selecting one or moretopics from the ranked candidate topics; and outputting the topics via auser interface (UI).
 2. The computer-implemented method of claim 1,further comprising: before determining a similarity score between thequery and each candidate topic based on a Vector Propagation On a ClickGraph (VPCG) model trained based on the user click data, performing afirst ranking to select a second one or more topics from the pluralityof candidate topics; in response to determining that each of the secondone or more topics does not meet a specified criterion, performing asecond ranking based on the VPCG model trained based on the user clickdata, wherein performing the second ranking comprises: the determining asimilarity score between the query and each candidate topic based on aVPCG model trained based on the user click data; and the ranking theplurality of candidate topics based on the similarity scores.
 3. Thecomputer-implemented method of claim 1, wherein the user click datacomprise a plurality of user selections in response to the plurality ofqueries from the plurality of users, wherein each of the plurality ofuser selections comprises a respective user's selection of a relevanttopic to the user's query among a plurality of candidate topics providedto the user based on the user's query.
 4. The computer-implementedmethod of claim 1, further comprising training the VPCG model based onthe user click data, wherein the training the VPCG model comprises:identifying a plurality of user selections in response to the pluralityof queries from the plurality of users in the user click data;determining a vector representation of each of the plurality of queriesin terms of a plurality of word elements in a vocabulary; anddetermining a vector representation of each of the plurality ofcandidate topics in terms of the plurality of word elements in thevocabulary.
 5. The computer-implemented method of claim 4, wherein: thedetermining a vector representation of each of the plurality of queriesin terms of a plurality of word elements in a vocabulary comprisesrepresenting the each of the plurality of queries using the plurality ofcandidate topics; and the determining a vector representation of each ofthe plurality of candidate topics in terms of the plurality of wordelements in the vocabulary comprises representing the each of theplurality of candidate topics using the plurality of queries.
 6. Thecomputer-implemented method of claim 1, wherein: the vectorrepresentation of each of the plurality of candidate topics based on thefirst vector representation of each of the plurality of queriescomprises a representation of each of the plurality of candidate topicsby a weighted sum of the plurality of queries according to equation:${d_{j}^{(n)} = {\frac{1}{{{\sum_{i = 1}^{Q}{C_{i,j} \cdot q_{i}^{({n - 1})}}}}_{2}}{\sum_{i = 1}^{Q}{C_{i,j} \cdot q_{i}^{({n - 1})}}}}};$and the second vector representation of each of the plurality of queriesbased on the vector representation of each of the plurality of candidatetopics comprises a representation of each of the plurality of queriesaccording to equation:${q_{i}^{(n)} = {\frac{1}{{{\sum_{j = 1}^{D}{C_{i,j} \cdot q_{j}^{({n - 1})}}}}_{2}}{\sum_{j = 1}^{D}{C_{i,j} \cdot d_{j}^{(n)}}}}},$wherein: d_(j) ^((n)) represents a vector representation of topic d_(j)in the (n)th interation; q_(i) ^((n-1)) represents a vectorrepresentation of query q_(i) in the (n−1) th interation; C_(i,j)represents a total number of user clicks or selections of the topicd_(j) for the query q_(i) in the user click data; Q represents a totalnumber of user queries in the user click data, i.e., q_(i), i=1, 2, . .. , Q; and D represents a total number of topics in the user click data,i.e., d_(j), j=1, 2, . . . , D.
 7. The computer-implemented method ofclaim 1, wherein the determining a similarity score between the queryand each of the plurality of candidate topics based on a VPCG modeltrained based on the user click data comprises: determining a vectorrepresentation of the query from the user in terms of a plurality ofword elements in a vocabulary; determining a vector representation ofeach of the plurality of candidate topics in terms of the plurality ofword elements in the vocabulary; and calculating the similarity scorebetween a vector representation of the query and the vectorrepresentation of each of the plurality of candidate topics.
 8. Thecomputer-implemented method of claim 7, wherein the determining a vectorrepresentation of the query from the user in terms of the plurality ofword elements in the vocabulary comprising representing the query fromthe user based on a set of ngrams trained based on the user click data.9. The computer-implemented method of claim 8, wherein the set of ngramscomprises a vector representation of each ngram in the set of ngrams interms of the plurality of word elements in the vocabulary based on thevector representation of each of a plurality of queries in the userclick data.
 10. The computer-implemented method of claim 8, wherein therepresenting the query from the user based on a set of ngrams trainedbased on the user click data comprises representing the query from theuser as a weighted sum the set of ngrams based on a vectorrepresentation of each ngram in the set of ngrams, and a weightcorresponding to the vector representation of each ngram.
 11. Acomputer-implemented system comprising: one or more computers; and oneor more computer memory devices interoperably coupled with the one ormore computers and having tangible, non-transitory, machine-readablemedia storing instructions, that when executed by the one or morecomputers, perform operations comprising: receiving, at a data serviceengine, a query from a user, wherein the query comprises a string ofcharacters; identifying, by the data service engine, a plurality ofcandidate topics based on the query; identifying a plurality of queriesfrom a plurality of users in user click data; determining a similarityscore between the query and each of the plurality of candidate topicsbased on a Vector Propagation On a Click Graph (VPCG) model trainedbased on the user click data, wherein the VPCG model comprises: a vectorrepresentation of each of the plurality of candidate topics based on afirst vector representation of each of the plurality of queries; and asecond vector representation of each of the plurality of queries basedon the vector representation of each of the plurality of candidatetopics; and ranking the plurality of candidate topics based on thesimilarity scores; selecting one or more topics from the rankedcandidate topics; and outputting the topics via a user interface (UI).12. The computer-implemented system of claim 11, the operations furthercomprising: before determining a similarity score between the query andeach candidate topic based on a Vector Propagation On a Click Graph(VPCG) model trained based on the user click data, performing a firstranking to select a second one or more topics from the plurality ofcandidate topics; in response to determining that each of the second oneor more topics does not meet a specified criterion, performing a secondranking based on the VPCG model trained based on the user click data,wherein performing the second ranking comprises: the determining asimilarity score between the query and each candidate topic based on aVPCG model trained based on the user click data; and the ranking theplurality of candidate topics based on the similarity scores.
 13. Thecomputer-implemented system of claim 11, wherein the user click datacomprise a plurality of user selections in response to the plurality ofqueries from the plurality of users, wherein each of the plurality ofuser selections comprises a respective user's selection of a relevanttopic to the user's query among a plurality of candidate topics providedto the user based on the user's query.
 14. The computer-implementedsystem of claim 11, the operations further comprising training the VPCGmodel based on the user click data, wherein the training the VPCG modelcomprises: identifying a plurality of user selections in response to theplurality of queries from the plurality of users in the user click data;determining a vector representation of each of the plurality of queriesin terms of a plurality of word elements in a vocabulary; anddetermining a vector representation of each of the plurality ofcandidate topics in terms of the plurality of word elements in thevocabulary.
 15. The computer-implemented system of claim 11, wherein thedetermining a similarity score between the query and each of theplurality of candidate topics based on a VPCG model trained based on theuser click data comprises: determining a vector representation of thequery from the user in terms of a plurality of word elements in avocabulary; determining a vector representation of each of the pluralityof candidate topics in terms of the plurality of word elements in thevocabulary; and calculating the similarity score between a vectorrepresentation of the query and the vector representation of each of theplurality of candidate topics.
 16. A non-transitory, computer-readablemedium storing one or more instructions executable by acomputer-implemented system to perform operations comprising: receiving,at a data service engine, a query from a user, wherein the querycomprises a string of characters; identifying, by the data serviceengine, a plurality of candidate topics based on the query; identifyinga plurality of queries from a plurality of users in user click data;determining a similarity score between the query and each of theplurality of candidate topics based on a Vector Propagation On a ClickGraph (VPCG) model trained based on the user click data, wherein theVPCG model comprises: a vector representation of each of the pluralityof candidate topics based on a first vector representation of each ofthe plurality of queries; and a second vector representation of each ofthe plurality of queries based on the vector representation of each ofthe plurality of candidate topics; and ranking the plurality ofcandidate topics based on the similarity scores; selecting one or moretopics from the ranked candidate topics; and outputting the topics via auser interface (UI).
 17. The non-transitory, computer-readable medium ofclaim 16, the operations further comprising: before determining asimilarity score between the query and each candidate topic based on aVector Propagation On a Click Graph (VPCG) model trained based on theuser click data, performing a first ranking to select a second one ormore topics from the plurality of candidate topics; in response todetermining that each of the second one or more topics does not meet aspecified criterion, performing a second ranking based on the VPCG modeltrained based on the user click data, wherein performing the secondranking comprises: the determining a similarity score between the queryand each candidate topic based on a VPCG model trained based on the userclick data; and the ranking the plurality of candidate topics based onthe similarity scores.
 18. The non-transitory, computer-readable mediumof claim 16, wherein the user click data comprise a plurality of userselections in response to a plurality of queries from a plurality ofusers, wherein each of the plurality of user selections comprises arespective user's selection of a relevant topic to the user's queryamong a plurality of candidate topics provided to the user based on theuser's query.
 19. The non-transitory, computer-readable medium of claim16, the operations further comprising training the VPCG model based onthe user click data, wherein the training the VPCG model comprises:identifying a plurality of user selections in response to the pluralityof queries from the plurality of users in the user click data;determining a vector representation of each of the plurality of queriesin terms of a plurality of word elements in a vocabulary; anddetermining a vector representation of each of the plurality ofcandidate topics in terms of the plurality of word elements in thevocabulary.
 20. The non-transitory, computer-readable medium of claim16, wherein the determining a similarity score between the query andeach of the plurality of candidate topics based on a VPCG model trainedbased on the user click data comprises: determining a vectorrepresentation of the query from the user in terms of a plurality ofword elements in a vocabulary; determining a vector representation ofeach of the plurality of candidate topics in terms of the plurality ofword elements in the vocabulary; and calculating the similarity scorebetween a vector representation of the query and the vectorrepresentation of each of the plurality of candidate topics.